Evolving agent-based simulations in the clouds

Evolving agent-based simulations enables one to automate the difficult iterative process of modeling complex adaptive systems to exhibit pre-specified/desired behaviors. Nevertheless this emerging technology, combining research advances in agent-based modeling/simulation and evolutionary computation, requires significant computing resources (i.e., high performance computing facilities) to evaluate simulation models across a large search space. Moreover, such experiments are typically conducted in an infrequent fashion and may occur when the computing facilities are not fully available. The user may thus be confronted with a computing budget limiting the use of these “evolvable simulation” techniques. We propose the use of the cloud computing paradigm to address these budget and flexibility issues. To assist this research, we utilize a modular evolutionary framework coined CASE (for complex adaptive system evolver) which is capable of evolving agent-based models using nature-inspired search algorithms. In this paper, we present an adaptation of this framework which supports the cloud computing paradigm. An example evolutionary experiment, which examines a simplified military scenario modeled with the agent-based simulation platform MANA, is presented. This experiment refers to Automated Red Teaming: a vulnerability assessment tool employed by defense analysts to study combat operations (which are regarded here as complex adaptive systems). The experimental results suggest promising research potential in exploiting the cloud computing paradigm to support computing intensive evolvable simulation experiments. Finally, we discuss an additional extension to our cloud computing compliant CASE in which we propose to incorporate a distributed evolutionary approach, e.g., the island-based model to further optimize the evolutionary search.